import torch
import math
import numpy as np
from torch import nn
from d2l import torch as d2l

'''生成数据集'''
max_degree = 20
n_train, n_test = 100, 100
true_w = np.zeros(max_degree)
true_w[0:4] = np.array([5, 1.2, -3.4, 5.6])

# normal高斯分布随机抽样
features = np.random.normal(size=(n_test + n_test, 1))
# shuffle随机打乱排序
np.random.shuffle(features)
poly_feature = np.power(features, np.arange(max_degree).reshape(1, -1))
for i in range(max_degree):
    poly_feature[:,i] /= math.gamma(i + 1)
# labels维度=(n_train_n_test)
labels = np.dot(poly_feature, true_w)
labels += np.random.normal(scale=0.1,size=labels.shape)

'''对模型进行训练测试'''
def evaluate_loss(net, data_iter, loss):
    """评估给定数据集上模型的损失"""
    metric = d2l.Accumulator(2) # 损失的总和,样本数量
    for X, y in data_iter:
        out = net(X)
        y = y.reshape(out.shape)
        l = loss(out, y)
        metric.add(l.sum(), l.numel())
    return metric[0] / metric[1]

def train(train_features, test_features, train_labels, test_labels,num_epochs=400):
    loss = nn.MSELoss(reduction='none')
    input_shape = train_features.shape[-1]
    # 不设置偏置，因为我们已经在多项式中实现了它
    net = nn.Sequential(nn.Linear(input_shape, 1, bias=False))
    batch_size = min(10, train_labels.shape[0])
    train_iter = d2l.load_array((train_features, train_labels.reshape(-1, 1)), batch_size)
    test_iter = d2l.load_array((test_features, test_labels.reshape(-1, 1)), batch_size, is_train=False)
    trainer = torch.optim.SGD(net.parameters(), lr=0.01)
    animator = d2l.Animator(xlabel='epoch', ylabel='loss', yscale='log', xlim=[1, num_epochs], ylim=[1e-3, 1e2], legend=['train', 'test'])
    for epoch in range(num_epochs):
        d2l.train_epoch_ch3(net, train_iter, loss, trainer)
    if epoch == 0 or (epoch + 1) % 20 == 0:
        animator.add(epoch + 1, (evaluate_loss(net, train_iter, loss), evaluate_loss(net, test_iter, loss)))
    print('weight:', net[0].weight.data.numpy())